confusion matrix in machine learning

1. Confusion Matrix Explained
Samuel Bohman

2. What is a Confusion Matrix?
A common method for describing the performance of a classification
model consisting of true positives, true negatives, false positives, and
false negatives.
It is called a confusion matrix because it shows how confused the
model is between the classes.

3. True Positives
Predicted class
Actual class
Apple Orange Pear
Apple 50 5 50
Orange 10 50 20
Pear 5 5 0
The model correctly classified 50 apples and 50 oranges.

4. True Negatives for Apple
The model correctly classified 75 cases as not belonging to class
apple.
Predicted class
Actual class
Apple Orange Pear
Apple 50 5 50
Orange 10 50 20
Pear 5 5 0

5. True Negatives for Orange
The model correctly classified 105 cases as not belonging to class
orange.
Predicted class
Actual class
Apple Orange Pear
Apple 50 5 50
Orange 10 50 20
Pear 5 5 0

6. True Negatives for Pear
The model correctly classified 115 cases as not belonging to class
pear.
Predicted class
Actual class
Apple Orange Pear
Apple 50 5 50
Orange 10 50 20
Pear 5 5 0

7. False Positives for Apple
The model incorrectly classified 15 cases as apples.
Predicted class
Actual class
Apple Orange Pear
Apple 50 5 50
Orange 10 50 20
Pear 5 5 0

8. False Positives for Orange
The model incorrectly classified 10 cases as oranges.
Predicted class
Actual class
Apple Orange Pear
Apple 50 5 50
Orange 10 50 20
Pear 5 5 0

9. False Positives for Pear
The model incorrectly classified 70 cases as pears.
Predicted class
Actual class
Apple Orange Pear
Apple 50 5 50
Orange 10 50 20
Pear 5 5 0

10. False Negatives for Apple
The model incorrectly classified 55 cases as not belonging to class
apple.
Predicted class
Actual class
Apple Orange Pear
Apple 50 5 50
Orange 10 50 20
Pear 5 5 0

11. False Negatives for Orange
The model incorrectly classified 30 cases as not belonging to class
orange.
Predicted class
Actual class
Apple Orange Pear
Apple 50 5 50
Orange 10 50 20
Pear 5 5 0

12. False Negatives for Pear
The model incorrectly classified 10 cases as not belonging to class
pears.
Predicted class
Actual class
Apple Orange Pear
Apple 50 5 50
Orange 10 50 20
Pear 5 5 0